Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm

NCT ID: NCT06796283

Last Updated: 2025-02-21

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 2628 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-04-26
• Study Completion Date: 2024-12-31

Brief Summary

The purpose of this observational study is to construct a recurrence risk prediction model for ischemic stroke within 1, 3, 6, and 12 months using XGBoost combined with Convolutional Neural Network (CNN) algorithm.

Method: Follow up was conducted on the study subjects at 1, 3, 6, and 12 months after discharge.

Follow up primary outcome: Whether the study subjects experienced recurrent stroke events.

Secondary outcome: Improved Rinkin score.

Collect information on research subjects:

It includes demographic data, physical examination, medical history, imaging images, medication use, scale scores, CYP2C19 genotype test results, laboratory tests, and other complex multidimensional data.

Detailed Description

This project conducts a one-year follow-up on the enrolled subjects (patients with ischemic stroke) to observe the recurrence and modified Rankin scores after discharge. Combining complex multidimensional data such as demographic information, physical examination, medical history, imaging images, medication status, NIHSS scale scores, Glasgow Coma Scale scores, CYP2C19 genotype test results, and laboratory examinations of the subjects; Convolutional Neural Networks (CNNs) are used to segment lesions and extract features from the subjects' imaging images; Cox regression models are employed to obtain factors influencing recurrence; a prediction model for the risk of recurrence within 1, 3, 6, and 12 months for ischemic stroke is constructed using the XGBoost algorithm combined with Convolutional Neural Networks. The exploration aims to provide new insights and methods for the prevention and control of major chronic diseases by evaluating the effectiveness of XGBoost in predicting the risk of ischemic stroke recurrence at different times.

1. Inclusion Criteria：

• 18-85 years old;
• Diagnosed with ischemic stroke or transient ischemic attack (diagnosis meets the criteria established by the Cerebrovascular Disease Group of the Neurology Branch of the Chinese Medical Association in 2014);
• During the acute phase of onset (2 weeks);
• Voluntarily participate and sign an informed consent form.

2. Exclusion criteria：

• Cancer patients;
• Cardiogenic infarction, cerebral infarction of other causes, and cerebral infarction of unknown causes;
• Patients with hemorrhagic stroke, mixed stroke, and tumor stroke;
• Merge with severe heart, lung, and liver system diseases;
• Researchers identify patients with poor compliance and inability to complete long-term follow-up;
• Patients currently participating in any clinical trials related to investigational drugs or medical devices.

Conditions

Ischemic Stroke

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• Aged between 18-85 years
• Diagnosed with ischemic stroke or transient ischemic attack (diagnosis in accordance with the standards set by the Cerebrovascular Disease Group of the Neurology Branch of the Chinese Medical Association in 2014)
• Within the acute phase of the illness (within 2 weeks)
• Voluntarily participates and signs an informed consent form

Exclusion Criteria

• Patients with cancer
• Patients with cardiogenic infarction, other causes of cerebral infarction, or cryptogenic cerebral infarction
• Patients with hemorrhagic stroke, mixed stroke, and tumor stroke
• Patients with severe heart, lung, or liver system diseases
• Patients judged by the researcher to have poor compliance and unable to complete long-term follow-up
• Patients currently participating in any clinical trials related to investigational drugs or medical devices

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 85 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Second Affiliated Hospital of Nanchang University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

yingping Y Yi

Role: PRINCIPAL_INVESTIGATOR

Second Affiliated Hospital of Nanchang University

Locations

The Second Affiliated Hospital of Nanchang University

Nanchang, Jiangxi, China

Countries

China

References

Liu J, Li J, Wu Y, Luo H, Yu P, Cheng R, Wang X, Xian H, Wu B, Chen Y, Ke J, Yi Y. Deep learning-based segmentation of acute ischemic stroke MRI lesions and recurrence prediction within 1 year after discharge: A multicenter study. Neuroscience. 2025 Jan 26;565:222-231. doi: 10.1016/j.neuroscience.2024.12.002. Epub 2024 Dec 2.

Reference Type: RESULT

PMID: 39631660 (View on PubMed)

Provided Documents

Document Type: Study Protocol, Statistical Analysis Plan, and Informed Consent Form

View Document

Related Links

https://pubmed.ncbi.nlm.nih.gov/39631660/

Explore deep - learning - based infarct lesion segmentation in AIS patients' brain MRI, radiomics - based 1 - year recurrence prediction, and develop a combined model for accurate AIS recurrence prediction

Other Identifiers

2021efyB03

Identifier Type: -

Identifier Source: org_study_id